The seven liberal arts (or “philosophia et septem rates liberales,” as they said back in the day). Image from Wikimedia Commons (License: CC-BY-SA 3.0)

In 2013, Oxford University economists Carl Frey and Michael Osborne published a study called The Future of Employment: How Susceptible Are Jobs to Computerization? The authors concluded that 47 percent of U.S. jobs were at a high risk of machine automation over the next two decades. It was a sobering and fear-inducing thought. And various versions of this apocalyptic prediction are heard more and more among not only professional prognosticators but also from tech leaders and workers impressed with their own software and hardware but fearful of the consequences for society.

Such fears are understandable, but they may be misleading. It isn’t that jobs are going away. Instead, jobs are inexorably changing as automation seeps ever deeper into society. We probably don’t need to worry about the existence of jobs per se, but rather about those who do not cultivate their ability to think broadly and continue refining the soft skills that are unique to being human.

The argument that masses of human workers will permanently lose jobs to machines —what’s called “technological unemployment”— has been made time and time again, notably at the dawn of the Industrial Revolution and during the Great Depression in 1930s. Economist John Maynard Keynes contended that job losses during the Depression due to technological advances were leading to “means of economizing the use of labor outrunning the pace at which we can find new uses for labor.” In other words, many would be without anything at all to do.

Of course this didn’t happen in anything like such a stark way. In 1900, approximately 40 percent of all American workers were employed on farms; today that number is just 2 percent. What the Industrial Revolution did was move farmers into factories and offices. But could this time be different?

Certainly we are seeing tremendous advances in artificial intelligence, robotics, and the routine automation of manual tasks. For example, many once flocked to high six-figure-salary jobs mining iron ore and gold in the Australian Outback, but today giant self-driving Volvo and Caterpillar trucks weighing close to a million pounds are instead scraping the earth there in open-pit mines. “An autonomous truck doesn’t need to stop for lunch breaks for shift changes,” Caterpillar’s marketing crows. Scania, another vehicle company, has pioneered trucks that use GPS and LIDAR (light detection and ranging) sensors to operate with optimal efficiency, minimizing fuel consumption. The trucks have improved efficiency by more than 15 percent. And of course we daily hear predictions that self-driving vehicles of all sorts are soon to replace human drivers of all sorts, especially the millions who drive for a living.

Machines often just seem to do things better. For example, vehicles like the CAT 797, a bright-yellow $5.5 million, four-thousand-horsepower truck that carries four hundred tons, breaks less often when computers are operating it. Careless human drivers burn through lots more rubber on the giant tires. That matters when each one costs more than $40,000.

Of course businesses cannot always just buy their way out of employing people. But in a highly controlled industry like mining, where worker safety and stamina is a major concern, and especially where work is highly routine, workers are moving from the pits into offices or unemployment. Machine automation saves mining companies money, so over time they will add more and more such equipment.

Routine work will be done in whatever way is safest and most efficient. And this applies not just to manual work but also increasingly to the cognitive work people do with their brains. But while routine tasks might go to machines, jobs are comprised of many elements. Only a small subset of what people do is likely routine enough to be scripted, programmed and performed by a machine or computer. Many tasks within all jobs are subject to enough variation that employers will prefer human labor to machines for a long time. Machine precision is laudable, but so too is human mutability. A machine might make a good burger, but is it also going to take out the trash?

The McKinsey Global Institute released a study in the summer of 2016 that analyzed the functions performed as part of 800 different occupations. Researchers looked at over 2,000 tasks performed across all these jobs and concluded that “while automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree.” McKinsey found that five percent of jobs could be fully automated (in stark contrast to the terrifying 47 percent figure in the Oxford study).

But the study concluded that we are likely to see a wholesale transformation of jobs, rather than their full replacement by machines and artificial intelligence. It found that roughly 30 percent of tasks within 60 percent of jobs would change. Researchers noted pointedly that this suggests that machines will augment our work environment rather than become our robot overlords anytime soon.

Those many tasks within a solid majority of jobs that will be immune to machine automation are those that cannot be sufficiently defined and programmed. Such tasks require creativity and original thought, intuition, coordination, communication, empathy and persuasion. In other words, humans might not perform rote tasks like guiding giant trucks to pick up piles of ore, or even elementary data collection. But they will ask questions of the data, help frame parameters, test hypotheses, collaborate with teammates across departments, and communicate results with compassion to clients.

In the hospital, nursing assistants today spend two-thirds of their time manually collecting health information. Over time, this job will certainly consist less of collecting patient vitals, because of course sensors do that quite well. But it’s presumptuous to believe seriously-ill patients would prefer even an empathetic robot to a caring human. In other words, the job of the nursing assistant will become more, not less, human. In the office, data and analytics will inform assessments of employee performance, but a manager will still coach and mentor his or her rising stars with care and hands-on attention. Humans will more and more interface with machines, but non-routine tasks will remain the purview of humans.

Because of the tremendous power of machines to supplement our abilities, the touch points between man and machine will continue to multiply. So we will definitely still need technical literacy, and in many cases solid STEM training. It’s of the utmost importance that these skills be nourished and prioritized across our communities. But it’s wrong to assume that basic technical training alone is sufficient to maintain relevance in tomorrow’s economy. For example, rote computer programming has already become a cheap commodity, purchased quickly and easily on the global market. And it is itself increasingly becoming automated.

At Harvard’s Graduate School of Education, David Deming is an economist who has looked at the change in relative employment for cognitive occupations over the past three decades. What he’s found is that the winners possess not pure math skills, or pure soft ones, but rather a blending of the two; what he calls “high math, high social.” Since 1980, jobs with a high requirement for social skills have grown significantly, whereas jobs with high math but low social abilities have actually declined. This is in part because in more complex work environments, worker specialization requires the trading and sharing of tasks, and soft skills reduce the so-called “transaction costs” of collaboration. Says James Manyika of the McKinsey Global Institute, who played a big role in its 2016 study: “People will be OK when it comes to jobs to the extent that they continue to focus on integrated systems-thinking skills. What people will need are problem-solving skills, learning to learn, and learning to adapt.”

So the question becomes: How do we cultivate human skills of adaptation, empathy, consideration for another’s perspective, and the ability to work together and communicate across differences? How do we train for a highly dynamic world where, for a college graduate today, it’s impossible even to imagine the jobs of 2060? Success and continued successful employment will come to those with both the technical literacy to understand machines as well as the soft skills to help maintain the human-to-human interface atop our techie world.

The answer to this challenge might be our least intuitive yet: at the moment of technological inflection, we need to double down on the liberal arts. After all, this is where students are exposed to broad ideas and challenged to grapple with the humanities, arts, and social and natural sciences in settings designed to tug on our minds, question our assumptions, and refine our curiosity. The liberal arts are not at odds with technical literacy. They are what give us the context with which we apply the new tools and our very human comparative advantage, even in a world in which machines continue to get smarter and smarter.